This Fact Sheet provides an overview of the Better Buildings Workforce Guidelines project. The Department of Energy (DOE) and the National Institute of Building Sciences (NIBS) are working with industry stakeholders to develop voluntary national guidelines that will improve the quality and consistency of commercial building workforce training and certification programs for five key energy-related jobs.
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The lack of empirical data on the energy performance of buildings is a key barrier to accelerating the energy efficiency retrofit market. The DOE’s Buildings Performance Database (BPD) helps address this gap by allowing users to perform exploratory analyses on an anonymous dataset of hundreds of thousands of commercial and residential buildings. These analyses enable market actors to assess energy efficiency opportunities, forecast project performance, and quantify performance risk using empirical building data. In this paper, we describe the process of collecting and preparing data for the database, and present a peer-group analysis tool that allows users to analyze building performance for narrowly defined subsets of the database, or peer groups. We use this tool to explore a case study of a multifamily portfolio owner comparing his buildings’ performance to the peer group of multifamily buildings in the local metro area. We also present a performance comparison tool that uses statistical methods to estimate the expected change in energy performance due to changes in building-component technologies. We demonstrate a low-effort retrofit analysis, providing a probabilistic estimate of energy savings for a sample building retrofit. The key advantages of this approach compared to conventional engineering models are that it provides probabilistic risk analysis based on actual
measured data and can significantly reduce transaction costs for predicting savings across a portfolio.
While the availability of “big data” about building energy performance is increasing in response to market demands and public policies, the lack of standard data formats is a significant ongoing barrier to its full utilization. To overcome this barrier, the U.S. Department of Energy (DOE) and Lawrence Berkeley National Laboratory (LBNL) developed the Building Energy Data Exchange Specification (BEDES).
BEDES is designed to enable the exchange, comparison, and combination of empirical information by providing common terms and definitions for data about commercial and residential building’s physical and operational characteristics, energy use, and efficiency measures.
This paper describes the BEDES development process, scope, structure, and plans for implementation and ongoing updates.
This multimedia toolkit is designed to guide energy efficiency program administrators through the process of planning, implementing and measuring a large-scale, deep retrofit energy efficiency program for small-to-medium businesses (SMB). We provide downloadable tools and forms you can adapt for use in your own program.
This guidebook is a reference to help other program sponsors and implementers develop and deliver a full-scale and comprehensive small-to-medium-sized business (SMB) energy efficiency program that can achieve similar results. The online SMART Scale Toolkit accompanies this guidebook.
A demonstration of the SMART Scale model in the Sacramento Municipal Utilities District (SMUD) on over 700 projects indicates that an average whole building electricity savings of 20% from the baseline is possible while remaining cost-effective, with a cost of $0.0346 per lifetime kWh and an estimated total resource cost of 3.1. Previous generations of DI programs were capturing only 10% to 12% of whole building electricity savings through approaches dominated by lighting measures.
In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14.
"The general concept of using meter data to quantify building energy savings is intuitive and straightforward; in practice, however, there are many complications. With support from DOE, LBNL has been working with partners to address many of the market and technical barriers for M&V 2.0."
This short blog article describes a related white paper titled "The Status and Promise of Advanced M&V: An Overview of 'M&V 2.0 Methods, Tools, and Applications" and a technical article titled "Application of Automated Measurement and Verification to Utility Energy Efficiency Program Data."
"The objective of this paper is to provide background information and frame key discussion points related to advanced M&V. The paper identifies the benefits, methods, and requirements of advanced M&V and outlines key technical issues for applying these methods. It presents an overview of the distinguishing elements of M&V 2.0 tools and of how the industry is addressing needs for tool testing, consistency, and standardization, and it identifies opportunities for collaboration."
Designed as a resource for those who want to develop community solar projects, from community organizers or solar energy advocates to government officials or utility managers. By exploring
the range of incentives and policies while providing examples of operational community solar projects, this guide will help communities to plan and implement successful local energy projects. In addition, by highlighting some of the policy best practices, this guide suggests changes in the regulatory landscape that could significantly boost community solar installations across the country.